Contactless Blood Pressure Measurement via Remote Photoplethysmography with Synthetic Data Generation Using Generative Adversarial Network

Bing Fei Wu, Li Wen Chiu, Yi Chiao Wu, Chun Chih Lai, Pao Hsien Chu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

16 Scopus citations

Abstract

Deriving blood pressure in a non-invasive way via photoplethysmography (PPG) signals has become a familiar topic. With the knowledge of the relation between PPG and blood pressure, we expect to further make the measurement contactless for convenience reasons. An alternative signal source is remote photoplethysmography (rPPG) signals. There are mainly two kinds of approaches for exploiting blood pressure through PPG signals, one is by calculating the pulse transit time of the arterial pulse wave at two consecutive sites and the other is based on waveform feature analysis from a single signal. The calibration procedure is necessary for the former way, which leads to some limitations in general use. On the other hand, the properties of the rPPG waveform are far from PPG signals. Hence, the known waveform features in PPG signals are hard to be leveraged in the case of rPPG signals. Recently, convolutional neural networks are also applied for solving this problem. However, the lack of data is an obstacle to the training procedure and evaluation. In this study, a multichannel rPPG-based blood pressure estimator is proposed. To ease the data scarcity issue, the generative adversarial network is adopted to augment synthetic waveform data. Besides, as we know that some physiological states like age and BMI are dominant factors in blood pressure. InfoGAN is chosen in this work to generate the synthetic data with the blood pressure value fluctuating correspondingly to the controlled age and BMI combination. The proposed model outperforms the state-of-the-art methods on MIMIC III and Cuffless datasets. With the synthetic data generation, the mean absolute error (MAE) is reduced to 6.72 and 5.95 mmHg in MAP and DBP respectively. The standard deviations of the MAEs are also reduced. In the rPPG case, the MAE of SBP is 9.13 and 8.76 mmHg for DBP.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
PublisherIEEE Computer Society
Pages2129-2137
Number of pages9
ISBN (Electronic)9781665487399
DOIs
StatePublished - 2022
Event2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 - New Orleans, United States
Duration: 19 06 202220 06 2022

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2022-June
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Conference

Conference2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Country/TerritoryUnited States
CityNew Orleans
Period19/06/2220/06/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

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